{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Image Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import cv2\n",
    "import torch\n",
    "import subprocess\n",
    "import platform\n",
    "import tqdm.autonotebook as tqdm\n",
    "\n",
    "import numpy as np\n",
    "import gradio as gr\n",
    "\n",
    "from basicsr.utils import img2tensor, tensor2img\n",
    "from basicsr.utils.registry import ARCH_REGISTRY\n",
    "from torchvision.transforms.functional import normalize\n",
    "from concurrent.futures import ThreadPoolExecutor\n",
    "\n",
    "\n",
    "# Custom Modules\n",
    "import audio\n",
    "import file_check\n",
    "import preprocess_mp as pmp\n",
    "import model_loaders as ml\n",
    "\n",
    "# Global Variables\n",
    "TEMP_DIRECTORY = file_check.TEMP_DIR\n",
    "MEDIA_DIRECTORY = file_check.MEDIA_DIR\n",
    "NPY_FILES_DIRECTORY = file_check.NPY_FILES_DIR\n",
    "OUTPUT_DIRECTORY = file_check.OUTPUT_DIR\n",
    "\n",
    "file_check.perform_check()\n",
    "\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ------------------ set up CodeFormer restorer -------------------\n",
    "net = ARCH_REGISTRY.get('CodeFormer')(dim_embd=512, codebook_size=1024, n_head=8, n_layers=9, \n",
    "                                        connect_list=['32', '64', '128', '256']).to(device)\n",
    "\n",
    "# ckpt_path = 'weights/CodeFormer/codeformer.pth'\n",
    "ckpt_path = file_check.CODEFORMERS_MODEL_PATH\n",
    "checkpoint = torch.load(ckpt_path)['params_ema']\n",
    "net.load_state_dict(checkpoint)\n",
    "net.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform checks to ensure that all required files are present\n",
    "file_check.perform_check()\n",
    "\n",
    "###################################################################################################################################################\n",
    "\n",
    "frame_path = r\"E:\\Lip_Wise_GFPGAN\\_testData\\Inputs\\test1.png\"\n",
    "audio_path = r'E:\\Lip_Wise_GFPGAN\\_testData\\Inputs\\a_for_apple.m4a'\n",
    "mel_step_size = 16\n",
    "fps = 25\n",
    "\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "\n",
    "###################################################################################################################################################\n",
    "\n",
    "# Get input type\n",
    "input_type, img_ext = file_check.get_file_type(frame_path)\n",
    "if input_type != \"image\":\n",
    "    raise Exception(\"Input file is not an image. Try again with an image file.\")\n",
    "\n",
    "# Get audio type\n",
    "audio_type, aud_ext = file_check.get_file_type(audio_path)\n",
    "if audio_type != \"audio\":\n",
    "    raise Exception(\"Input file is not an audio.\")\n",
    "if aud_ext != \"wav\":\n",
    "    print(\"Audio file is not a wav file. Converting to wav...\")\n",
    "    # Convert audio to wav\n",
    "    command = 'ffmpeg -y -i {} -strict -2 {}'.format(audio_path, os.path.join(MEDIA_DIRECTORY, 'aud_input.wav'))\n",
    "    subprocess.call(command, shell=True)\n",
    "    audio_path = os.path.join(MEDIA_DIRECTORY, 'aud_input.wav')\n",
    "\n",
    "# Check for cuda\n",
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "print(f'Using {device} for inference.')\n",
    "\n",
    "# Generate audio spectrogram\n",
    "print(\"Generating audio spectrogram...\")\n",
    "wav = audio.load_wav(audio_path, 16000)\n",
    "mel = audio.melspectrogram(wav)\n",
    "\n",
    "mel_chunks = []\n",
    "#The mel_idx_multiplier aligns audio chunks with video frames for consistent processing and analysis.\n",
    "mel_idx_multiplier = 80./fps\n",
    "i = 0\n",
    "while 1:\n",
    "    start_idx = int(i * mel_idx_multiplier)\n",
    "    if start_idx + mel_step_size > len(mel[0]):\n",
    "        mel_chunks.append(mel[:, len(mel[0]) - mel_step_size:])\n",
    "        break\n",
    "    mel_chunks.append(mel[:, start_idx : start_idx + mel_step_size])\n",
    "    i += 1\n",
    "\n",
    "print(f\"Length of mel chunks: {len(mel_chunks)}\")\n",
    "\n",
    "# Create media_preprocess object and helper object\n",
    "processor = pmp.model_processor()\n",
    "\n",
    "# Read image\n",
    "frame = cv2.imread(frame_path)\n",
    "height, width, _ = frame.shape\n",
    "\n",
    "#######################################################################\n",
    "a = fig.add_subplot(3, 3, 1)\n",
    "a.imshow(frame[:, :, ::-1])\n",
    "a.set_title('Original Image')\n",
    "a.axis('off')\n",
    "#######################################################################\n",
    "\n",
    "# Get face landmarks\n",
    "print(\"Getting face landmarks...\")\n",
    "processor.detect_for_image(frame.copy())\n",
    "\n",
    "# Create face helper object from landmarks\n",
    "helper = pmp.FaceHelpers(image_mode=True)\n",
    "\n",
    "# extract face from image\n",
    "print(\"Extracting face from image...\")\n",
    "extracted_face, original_mask = helper.extract_face(frame.copy())\n",
    "\n",
    "#######################################################################\n",
    "b = fig.add_subplot(3, 3, 2)\n",
    "b.imshow(extracted_face[:, :, ::-1])\n",
    "b.set_title('extracted_face')\n",
    "b.axis('off')\n",
    "#######################################################################\n",
    "\n",
    "# warp and align face\n",
    "print(\"Warping and aligning face...\")\n",
    "aligned_face, rotation_matrix = helper.alignment_procedure(extracted_face)\n",
    "\n",
    "#######################################################################\n",
    "c = fig.add_subplot(3, 3, 3)\n",
    "c.imshow(aligned_face[:, :, ::-1])\n",
    "c.set_title('aligned_face')\n",
    "c.axis('off')\n",
    "\n",
    "print(aligned_face.shape)\n",
    "#######################################################################\n",
    "\n",
    "# Crop face\n",
    "print(\"Cropping face...\")\n",
    "cropped_face, bbox = helper.crop_extracted_face(aligned_face, rotation_matrix)\n",
    "\n",
    "#######################################################################\n",
    "d = fig.add_subplot(3, 3, 4)\n",
    "d.imshow(cropped_face[:, :, ::-1])\n",
    "d.set_title('cropped_face')\n",
    "d.axis('off')\n",
    "#######################################################################\n",
    "\n",
    "# Store cropped face's height and width\n",
    "cropped_face_height, cropped_face_width, _ = cropped_face.shape\n",
    "\n",
    "# Generate data for inference\n",
    "gen = helper.gen_data_image_mode(cropped_face, mel_chunks)\n",
    "\n",
    "# Load wav2lip model\n",
    "w2l_model = ml.load_wav2lip_model()\n",
    "\n",
    "# Load GFPGAN model\n",
    "gfpgan = ml.load_gfpgan_model()\n",
    "gfpgan = gfpgan.to(device)\n",
    "\n",
    "# Initialize video writer\n",
    "out = cv2.VideoWriter(os.path.join(MEDIA_DIRECTORY, 'temp.mp4'), cv2.VideoWriter_fourcc(*'mp4v'), fps, (width, height))\n",
    "\n",
    "# Feed to model:\n",
    "for i, (img_batch, mel_batch) in enumerate(gen):\n",
    "    img_batch = torch.FloatTensor(np.transpose(img_batch, (0, 3, 1, 2))).to(device)\n",
    "    mel_batch = torch.FloatTensor(np.transpose(mel_batch, (0, 3, 1, 2))).to(device)\n",
    "\n",
    "    with torch.no_grad():\n",
    "        dubbed_faces = w2l_model(mel_batch, img_batch)\n",
    "    \n",
    "    dubbed_faces = dubbed_faces.cpu().numpy().transpose(0, 2, 3, 1) * 255.\n",
    "\n",
    "    for i, d in enumerate(dubbed_faces):\n",
    "        d = cv2.resize(d.astype(np.uint8) / 255., (512, 512), interpolation=cv2.INTER_CUBIC)\n",
    "        ################################################################################################\n",
    "        # e = fig.add_subplot(3, 3, 5+i)\n",
    "        # e.imshow(d[:, :, ::-1])\n",
    "        # e.set_title('dubbed_face')\n",
    "        # e.axis('off')\n",
    "\n",
    "        # plt.show()\n",
    "        ################################################################################################\n",
    "        dubbed_face_t = img2tensor(d, bgr2rgb=True, float32=True)\n",
    "        normalize(dubbed_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)\n",
    "        dubbed_face_t = dubbed_face_t.unsqueeze(0).to(device)\n",
    "        \n",
    "        try:\n",
    "            # output = gfpgan(dubbed_face_t, return_rgb=False, weight=0.5)[0] # for GFPGANv1\n",
    "            with torch.no_grad():\n",
    "                output = net(dubbed_face_t, w=0.5, adain=True)[0] # for CodeFormer\n",
    "                restored_face = tensor2img(output.squeeze(0), rgb2bgr=True, min_max=(-1, 1))\n",
    "            del output\n",
    "            torch.cuda.empty_cache()\n",
    "        except RuntimeError as error:\n",
    "            print(f'\\tFailed inference for GFPGAN: {error}.')\n",
    "            restored_face = d\n",
    "        \n",
    "        restored_face = restored_face.astype(np.uint8)\n",
    "\n",
    "        # Warp face back to original pose\n",
    "        \n",
    "        processed_face = cv2.resize(restored_face, (cropped_face_width, cropped_face_height), interpolation=cv2.INTER_LANCZOS4)\n",
    "        processed_ready = helper.paste_back_black_bg(processed_face, bbox, extracted_face)\n",
    "        ready_to_paste = helper.unwarp_align(processed_ready, rotation_matrix)\n",
    "        restored_image = helper.paste_back(ready_to_paste, frame, original_mask)\n",
    "\n",
    "        out.write(restored_image)\n",
    "    \n",
    "out.release()\n",
    "\n",
    "command = f\"ffmpeg -y -i {audio_path} -i {os.path.join(MEDIA_DIRECTORY, 'temp.mp4')} -strict -2 -q:v 1 {os.path.join(OUTPUT_DIRECTORY, 'output.mp4')}\"\n",
    "subprocess.call(command, shell=platform.system() != 'Windows')\n",
    "\n",
    "result_path = os.path.join(OUTPUT_DIRECTORY, 'output.mp4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cf_restorer(d):\n",
    "    d = cv2.resize(d.astype(np.uint8) / 255., (512, 512), interpolation=cv2.INTER_CUBIC)\n",
    "    dubbed_face_t = img2tensor(d, bgr2rgb=True, float32=True)\n",
    "    normalize(dubbed_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)\n",
    "    dubbed_face_t = dubbed_face_t.unsqueeze(0).to(device)\n",
    "    \n",
    "    try:\n",
    "        with torch.no_grad():\n",
    "            output = net(dubbed_face_t, w=0.7, adain=True)[0] # for CodeFormer\n",
    "            restored_face = tensor2img(output.squeeze(0), rgb2bgr=True, min_max=(-1, 1))\n",
    "        del output\n",
    "        torch.cuda.empty_cache()\n",
    "    except RuntimeError as error:\n",
    "        print(f'\\tFailed inference for GFPGAN: {error}.')\n",
    "        restored_face = tensor2img(dubbed_face_t, rgb2bgr=True, min_max=(-1, 1))\n",
    "    \n",
    "    restored_face = restored_face.astype(np.uint8)\n",
    "    return restored_face"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def infer_image(frame_path, audio_path, fps=30, mel_step_size=16, weight = 1.0):\n",
    "    \n",
    "    # Perform checks to ensure that all required files are present\n",
    "    file_check.perform_check()\n",
    "\n",
    "    # Get input type\n",
    "    input_type, img_ext = file_check.get_file_type(frame_path)\n",
    "    if input_type != \"image\":\n",
    "        raise Exception(\"Input file is not an image. Try again with an image file.\")\n",
    "    \n",
    "    # Get audio type\n",
    "    audio_type, aud_ext = file_check.get_file_type(audio_path)\n",
    "    if audio_type != \"audio\":\n",
    "        raise Exception(\"Input file is not an audio.\")\n",
    "    if aud_ext != \"wav\":\n",
    "        print(\"Audio file is not a wav file. Converting to wav...\")\n",
    "        # Convert audio to wav\n",
    "        command = 'ffmpeg -y -i {} -strict -2 {}'.format(audio_path, os.path.join(MEDIA_DIRECTORY, 'aud_input.wav'))\n",
    "        subprocess.call(command, shell=True)\n",
    "        audio_path = os.path.join(MEDIA_DIRECTORY, 'aud_input.wav')\n",
    "    \n",
    "    # Check for cuda\n",
    "    device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "    print(f'Using {device} for inference.')\n",
    "\n",
    "    # Generate audio spectrogram\n",
    "    print(\"Generating audio spectrogram...\")\n",
    "    wav = audio.load_wav(audio_path, 16000)\n",
    "    mel = audio.melspectrogram(wav)\n",
    "\n",
    "    mel_chunks = []\n",
    "    #The mel_idx_multiplier aligns audio chunks with video frames for consistent processing and analysis.\n",
    "    mel_idx_multiplier = 80./fps\n",
    "    i = 0\n",
    "    while 1:\n",
    "        start_idx = int(i * mel_idx_multiplier)\n",
    "        if start_idx + mel_step_size > len(mel[0]):\n",
    "            mel_chunks.append(mel[:, len(mel[0]) - mel_step_size:])\n",
    "            break\n",
    "        mel_chunks.append(mel[:, start_idx : start_idx + mel_step_size])\n",
    "        i += 1\n",
    "\n",
    "    print(f\"Length of mel chunks: {len(mel_chunks)}\")\n",
    "\n",
    "    # Create media_preprocess object and helper object\n",
    "    processor = pmp.model_processor()\n",
    "\n",
    "    # Read image\n",
    "    frame = cv2.imread(frame_path)\n",
    "    height, width, _ = frame.shape\n",
    "\n",
    "    # Get face landmarks\n",
    "    print(\"Getting face landmarks...\")\n",
    "    processor.detect_for_image(frame.copy())\n",
    "\n",
    "    # Create face helper object from landmarks\n",
    "    helper = pmp.FaceHelpers(image_mode=True)\n",
    "\n",
    "    # extract face from image\n",
    "    print(\"Extracting face from image...\")\n",
    "    extracted_face, original_mask = helper.extract_face(frame.copy())\n",
    "\n",
    "    # warp and align face\n",
    "    print(\"Warping and aligning face...\")\n",
    "    aligned_face, rotation_matrix = helper.alignment_procedure(frame.copy()) \n",
    "\n",
    "    # Crop face\n",
    "    print(\"Cropping face...\")\n",
    "    cropped_face, bbox = helper.crop_extracted_face(aligned_face, rotation_matrix)\n",
    "\n",
    "    # Store cropped face's height and width\n",
    "    cropped_face_height, cropped_face_width, _ = cropped_face.shape\n",
    "\n",
    "    # Generate data for inference\n",
    "    gen = helper.gen_data_image_mode(cropped_face, mel_chunks)\n",
    "\n",
    "    # Load wav2lip model\n",
    "    w2l_model = ml.load_wav2lip_model()\n",
    "\n",
    "    # Load GFPGAN model\n",
    "    # gfpgan = ml.load_gfpgan_model()\n",
    "    # gfpgan = gfpgan.to(device)\n",
    "\n",
    "    # Initialize video writer\n",
    "    out = cv2.VideoWriter(os.path.join(MEDIA_DIRECTORY, 'temp.mp4'), cv2.VideoWriter_fourcc(*'mp4v'), fps, (width, height))\n",
    "\n",
    "    # Feed to model:\n",
    "    for i, (img_batch, mel_batch) in enumerate(gr.Progress(track_tqdm=True).tqdm(gen)):\n",
    "        img_batch = torch.FloatTensor(np.transpose(img_batch, (0, 3, 1, 2))).to(device)\n",
    "        mel_batch = torch.FloatTensor(np.transpose(mel_batch, (0, 3, 1, 2))).to(device)\n",
    "\n",
    "        with torch.no_grad():\n",
    "            dubbed_faces = w2l_model(mel_batch, img_batch)\n",
    "        \n",
    "        dubbed_faces = dubbed_faces.cpu().numpy().transpose(0, 2, 3, 1) * 255.\n",
    "\n",
    "        with ThreadPoolExecutor(max_workers=2) as executor:\n",
    "            restored_faces = list(executor.map(cf_restorer, dubbed_faces))\n",
    "\n",
    "            # Warp face back to original pose\n",
    "        for i, restored_face in enumerate(restored_faces):\n",
    "            \n",
    "            processed_face = cv2.resize(restored_face, (cropped_face_width, cropped_face_height), interpolation=cv2.INTER_LANCZOS4)\n",
    "            processed_ready = helper.paste_back_black_bg(processed_face, bbox, extracted_face)\n",
    "            ready_to_paste = helper.unwarp_align(processed_ready, rotation_matrix)\n",
    "            restored_image = helper.paste_back(ready_to_paste, frame, original_mask)\n",
    "\n",
    "        out.write(restored_image)\n",
    "        \n",
    "    out.release()\n",
    "\n",
    "    command = f\"ffmpeg -y -i {audio_path} -i {os.path.join(MEDIA_DIRECTORY, 'temp.mp4')} -strict -2 -q:v 1 {os.path.join(OUTPUT_DIRECTORY, 'output.mp4')}\"\n",
    "    subprocess.call(command, shell=platform.system() != 'Windows')\n",
    "\n",
    "    return os.path.join(OUTPUT_DIRECTORY, 'output.mp4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if __name__ == \"__main__\":\n",
    "    # Create interface\n",
    "    inputs = [\n",
    "        gr.Image(type=\"filepath\", label=\"Image\"),\n",
    "        gr.Audio(type=\"filepath\", label=\"Audio\"),\n",
    "        gr.Slider(minimum=1, maximum=60, step=1, value=30, label=\"FPS\"),\n",
    "        gr.Slider(minimum=0, maximum=160, step=16, value=16, label=\"Mel Step Size\"),\n",
    "        gr.Slider(minimum=0, maximum=1, step=0.1, value=0.7, label=\"Codeformer_Fidelity (0 for better quality, 1 for better identity)\")\n",
    "    ]\n",
    "    outputs = gr.Video(sources='upload', label=\"Output\")\n",
    "    title = \"Lip Wise\"\n",
    "\n",
    "    gr.Interface(fn=infer_image, inputs=inputs, outputs=outputs, title=title).launch()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Video Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frames to process: 1803\n",
      "Frame 0 processing\n",
      "Face Detected: True\n",
      "Frame 0 processed\n",
      "Frame 1 processing\n",
      "Face Detected: True\n",
      "Frame 1 processed\n",
      "Frame 2 processing\n",
      "Face Detected: True\n",
      "Frame 2 processed\n",
      "Frame 3 processing\n",
      "Face Detected: True\n",
      "Frame 3 processed\n",
      "Frame 4 processing\n",
      "Face Detected: True\n",
      "Frame 4 processed\n",
      "Frame 5 processing\n",
      "Face Detected: True\n",
      "Frame 5 processed\n",
      "Frame 6 processing\n",
      "Face Detected: True\n",
      "Frame 6 processed\n",
      "Frame 7 processing\n",
      "Face Detected: True\n",
      "Frame 7 processed\n",
      "Frame 8 processing\n",
      "Face Detected: True\n",
      "Frame 8 processed\n",
      "Frame 9 processing\n",
      "Face Detected: True\n",
      "Frame 9 processed\n",
      "Frame 10 processing\n",
      "Face Detected: True\n",
      "Frame 10 processed\n",
      "Frame 11 processing\n",
      "Face Detected: True\n",
      "Frame 11 processed\n",
      "Frame 12 processing\n",
      "Face Detected: True\n",
      "Frame 12 processed\n",
      "Frame 13 processing\n",
      "Face Detected: True\n",
      "Frame 13 processed\n",
      "Frame 14 processing\n",
      "Face Detected: True\n",
      "Frame 14 processed\n",
      "Frame 15 processing\n",
      "Face Detected: True\n",
      "Frame 15 processed\n",
      "Frame 16 processing\n",
      "Face Detected: True\n",
      "Frame 16 processed\n",
      "Frame 17 processing\n",
      "Face Detected: True\n",
      "Frame 17 processed\n",
      "Frame 18 processing\n",
      "Face Detected: True\n",
      "Frame 18 processed\n",
      "Frame 19 processing\n",
      "Face Detected: True\n",
      "Frame 19 processed\n",
      "Frame 20 processing\n",
      "Face Detected: True\n",
      "Frame 20 processed\n",
      "Frame 21 processing\n",
      "Face Detected: True\n",
      "Frame 21 processed\n",
      "Frame 22 processing\n",
      "Face Detected: True\n",
      "Frame 22 processed\n",
      "Frame 23 processing\n",
      "Face Detected: True\n",
      "Frame 23 processed\n",
      "Frame 24 processing\n",
      "Face Detected: True\n",
      "Frame 24 processed\n",
      "Frame 25 processing\n",
      "Face Detected: True\n",
      "Frame 25 processed\n",
      "Frame 26 processing\n",
      "Face Detected: True\n",
      "Frame 26 processed\n",
      "Frame 27 processing\n",
      "Face Detected: True\n",
      "Frame 27 processed\n",
      "Frame 28 processing\n",
      "Face Detected: True\n",
      "Frame 28 processed\n",
      "Frame 29 processing\n",
      "Face Detected: True\n",
      "Frame 29 processed\n",
      "Frame 30 processing\n",
      "Face Detected: True\n",
      "Frame 30 processed\n",
      "Frame 31 processing\n",
      "Face Detected: True\n",
      "Frame 31 processed\n",
      "Frame 32 processing\n",
      "Face Detected: True\n",
      "Frame 32 processed\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: False\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
      "Frame 1114 processed\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
      "Frame 1118 processed\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
      "Frame 1165 processed\n",
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      "Face Detected: False\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Frame 1269 processing\n",
      "Face Detected: True\n",
      "Frame 1269 processed\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
      "Frame 1749 processed\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
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      "Face Detected: True\n",
      "Frame 1798 processed\n",
      "Frame 1799 processing\n",
      "Face Detected: True\n",
      "Frame 1799 processed\n",
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      "Face Detected: True\n",
      "Frame 1800 processed\n",
      "Frame 1801 processing\n",
      "Face Detected: True\n",
      "Frame 1801 processed\n",
      "Frame 1802 processing\n",
      "Face Detected: False\n",
      "Frame 1802 processed\n"
     ]
    }
   ],
   "source": [
    "import mediapipe as mp\n",
    "import cv2\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "import file_check\n",
    "\n",
    "# Get video path\n",
    "video_path = r'E:\\Lip_Wise_GFPGAN\\_testData\\Inputs\\small_test.mp4'\n",
    "\n",
    "video = cv2.VideoCapture(video_path)\n",
    "\n",
    "# Get video properties\n",
    "fps = video.get(cv2.CAP_PROP_FPS)\n",
    "frame_count = int(video.get(cv2.CAP_PROP_FRAME_COUNT))\n",
    "width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))\n",
    "height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))\n",
    "\n",
    "print(f\"Frames to process: {frame_count}\")\n",
    "\n",
    "# Initialize mediapipe\n",
    "BaseOptions = mp.tasks.BaseOptions\n",
    "VisionRunningMode = mp.tasks.vision.RunningMode\n",
    "\n",
    "FaceLandmarker = mp.tasks.vision.FaceLandmarker\n",
    "FaceLandmarkerOptions = mp.tasks.vision.FaceLandmarkerOptions\n",
    "\n",
    "FaceDetector = mp.tasks.vision.FaceDetector\n",
    "FaceDetectorOptions = mp.tasks.vision.FaceDetectorOptions\n",
    "\n",
    "# Create a face detector instance with the image mode:\n",
    "options_det = FaceDetectorOptions(\n",
    "    base_options=BaseOptions(model_asset_path=file_check.MP_DETECTOR_MODEL_PATH),\n",
    "    min_detection_confidence=0.5,\n",
    "    running_mode=VisionRunningMode.IMAGE)\n",
    "\n",
    "\n",
    "options_lan = FaceLandmarkerOptions(\n",
    "    base_options=BaseOptions(model_asset_path=file_check.MP_LANDMARKER_MODEL_PATH),\n",
    "    min_face_detection_confidence=0.5,\n",
    "    running_mode=VisionRunningMode.IMAGE)\n",
    "\n",
    "frame_no = 0\n",
    "no_face_index = []\n",
    "video_landmarks = np.zeros((frame_count, 486, 2)).astype(np.float64)\n",
    "\n",
    "with FaceLandmarker.create_from_options(options_lan) as landmarker,FaceDetector.create_from_options(options_det) as detector:\n",
    "    while video.isOpened():\n",
    "\n",
    "        ret, frame = video.read()\n",
    "        \n",
    "        if not ret:\n",
    "            break\n",
    "        \n",
    "        # Get frame timestamp\n",
    "        timestamp = int(video.get(cv2.CAP_PROP_POS_MSEC))\n",
    "\n",
    "        # Convert frame to RGB and convert to MediaPipe image\n",
    "        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
    "        mp_frame = mp.Image(image_format=mp.ImageFormat.SRGB, data=frame)\n",
    "\n",
    "        # Run face detector and face landmark models in IMAGE mode\n",
    "        result_landmarker = landmarker.detect(mp_frame)\n",
    "        result_detection = detector.detect(mp_frame)\n",
    "\n",
    "        # Get data ready to be saved\n",
    "        print(f\"Frame {frame_no} processing\")\n",
    "        print(f\"Face Detected: {len(result_landmarker.face_landmarks) > 0}\")\n",
    "        \n",
    "        if len(result_detection.detections) > 0 and len(result_landmarker.face_landmarks) > 0:\n",
    "            # Get bounding box\n",
    "            bbox = result_detection.detections[0].bounding_box\n",
    "            bbox_np = np.array([[bbox.origin_x, bbox.origin_y], [bbox.origin_x + bbox.width, bbox.origin_y + bbox.height]])\n",
    "            bbox_np = bbox_np + [[0, -10], [0, 10]] # To include more forehead and full chin\n",
    "            bbox_np = (bbox_np/[width, height]).astype(np.float64) # Normalize\n",
    "\n",
    "            # Get Keypoints\n",
    "            kp = result_detection.detections[0].keypoints\n",
    "            kp_np = np.array([[k.x, k.y] for k in kp]).astype(np.float64)\n",
    "\n",
    "            # Get landmarks\n",
    "            landmarks_np = np.array([[i.x, i.y] for i in result_landmarker.face_landmarks[0]]).astype(np.float64)\n",
    "\n",
    "            # Concatenate landmarks, bbox and keypoints. This is the data that will be saved.\n",
    "            data = np.vstack((landmarks_np, bbox_np, kp_np)).astype(np.float64)\n",
    "        else:\n",
    "            data = np.zeros((486,2)).astype(np.float64)\n",
    "            no_face_index.append(frame_no)\n",
    "\n",
    "        # Append data\n",
    "        print(f\"Frame {frame_no} processed\")\n",
    "        video_landmarks[frame_no] = data\n",
    "    \n",
    "        # Increment frame number\n",
    "        frame_no += 1\n",
    "        \n",
    "    # Save video landmarks\n",
    "    np.save(os.path.join(file_check.NPY_FILES_DIR,'video_landmarks.npy'), video_landmarks)\n",
    "    np.save(os.path.join(file_check.NPY_FILES_DIR,'no_face_index.npy'), np.array(no_face_index))\n",
    "\n",
    "    # Release video\n",
    "    video.release()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "face_index_list: [   0    1    2 ... 1799 1800 1801]\n"
     ]
    }
   ],
   "source": [
    "# Generating face_index_list\n",
    "a1 = np.arange(0, frame_count)\n",
    "a2 = np.load(os.path.join(file_check.NPY_FILES_DIR,'no_face_index.npy'))\n",
    "\n",
    "difference = np.in1d(a1, a2, invert=True)\n",
    "face_index_list = a1[difference]\n",
    "\n",
    "print(f\"face_index_list: {face_index_list}\")\n",
    "np.save(os.path.join(file_check.NPY_FILES_DIR,'face_index_list.npy'), face_index_list)\n",
    "np.savetxt(os.path.join(file_check.NPY_FILES_DIR,'face_index_list.txt'), face_index_list, fmt='%d')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   0],\n",
       "       [   1],\n",
       "       [   2],\n",
       "       ...,\n",
       "       [1799],\n",
       "       [1800],\n",
       "       [1801]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "face_index_list.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Segment start: 0, Segment end: 276\n",
      "Segment start: 662, Segment end: 818\n",
      "Segment start: 922, Segment end: 1166\n",
      "Segment start: 1167, Segment end: 1802\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Calculate the difference between consecutive elements\n",
    "diff = np.diff(face_index_list)\n",
    "\n",
    "# Find the indices where the difference is greater than 1\n",
    "breaks = np.where(diff > 1)[0]\n",
    "\n",
    "# The start of each segment is one index after each break\n",
    "segment_starts = np.insert(face_index_list[breaks + 1], 0, face_index_list[0])\n",
    "\n",
    "# The end of each segment is one index before each break\n",
    "segment_ends = np.append(face_index_list[breaks], face_index_list[-1])\n",
    "\n",
    "# Print the start and end of each segment\n",
    "for start, end in zip(segment_starts, segment_ends):\n",
    "    print(f\"Segment start: {start}, Segment end: {end+1}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'os' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[4], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m b \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mload(\u001b[43mos\u001b[49m\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(file_check\u001b[38;5;241m.\u001b[39mNPY_FILES_DIR,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mno_face_index.npy\u001b[39m\u001b[38;5;124m'\u001b[39m))\n\u001b[0;32m      2\u001b[0m np\u001b[38;5;241m.\u001b[39msavetxt(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(file_check\u001b[38;5;241m.\u001b[39mNPY_FILES_DIR,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mno_face_index.txt\u001b[39m\u001b[38;5;124m'\u001b[39m), b, fmt\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'os' is not defined"
     ]
    }
   ],
   "source": [
    "b = np.load(os.path.join(file_check.NPY_FILES_DIR,'no_face_index.npy'))\n",
    "np.savetxt(os.path.join(file_check.NPY_FILES_DIR,'no_face_index.txt'), b, fmt='%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "segments: [(0, 5), (5, 6), (6, 8), (8, 9), (9, 24), (24, 28), (28, 46), (46, 108), (108, 154), (154, 161), (161, 177), (177, 1534), (1534, 1555), (1555, 1556), (1556, 1557), (1557, 1574), (1574, 1576), (1576, 1577), (1577, 1589), (1589, 1718), (1718, 1750), (1750, 2526), (2526, 2527), (2527, 2534), (2534, 2536), (2536, 2558), (2558, 2561), (2561, 2899), (2899, 3405), (3405, 3408), (3408, 3422), (3422, 3497), (3497, 3498), (3498, 4987)]\n"
     ]
    }
   ],
   "source": [
    "def find_continuous_segments(arr):\n",
    "    breaks = np.where(np.diff(arr) != 1)[0] + 1  # Find breaks in continuity\n",
    "    segments = []\n",
    "    start = 0\n",
    "    for break_idx in breaks:\n",
    "        end = break_idx\n",
    "        segments.append((start, end))\n",
    "        start = end\n",
    "    segments.append((start, len(arr)))  # Add the last segment\n",
    "    return segments\n",
    "\n",
    "segments = find_continuous_segments(face_index_list)\n",
    "\n",
    "np.save(os.path.join(file_check.NPY_FILES_DIR,'index_segments.npy'), np.array(segments))\n",
    "print(f\"segments: {segments}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "try:\n",
    "    cap = cv2.VideoCapture(r\"E:\\Lip_Wise_GFPGAN\\_testData\\Inputs\\small_test.mp4\")\n",
    "except Exception as e:\n",
    "    print(e)\n",
    "# no_face_index = np.load(r\"E:\\Lip_Wise_GFPGAN\\temp\\npy_files\\no_face_index.npy\")\n",
    "# face_index_list = np.load(r\"E:\\Lip_Wise_GFPGAN\\temp\\npy_files\\face_index_list.npy\")\n",
    "\n",
    "fps = cap.get(cv2.CAP_PROP_FPS)\n",
    "height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n",
    "width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n",
    "frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n",
    "batch_size = 500\n",
    "\n",
    "print(f\"fps: {fps}\")\n",
    "print(f\"height: {height}\")\n",
    "print(f\"width: {width}\")\n",
    "print(f\"frame_count: {frame_count}\")\n",
    "\n",
    "try:\n",
    "    frames = np.zeros((batch_size, height, width, 3), np.dtype('uint8'))\n",
    "except MemoryError as error:\n",
    "    print(f\"Memory Error: {error}\")\n",
    "    batch_size = 100\n",
    "    frames = np.zeros((batch_size, height, width, 3), np.dtype('uint8'))\n",
    "\n",
    "# face_frames = np.empty((batch_size, height, width, 3), np.dtype('uint8'))\n",
    "i = 0\n",
    "while True:\n",
    "    \n",
    "    ret, frame = cap.read()\n",
    "    \n",
    "    if not ret:\n",
    "        break\n",
    "    \n",
    "    frames[i] = frame\n",
    "    plt.imshow(frames[i][:, :, ::-1])\n",
    "\n",
    "    print(frames[i])\n",
    "\n",
    "    # if i in no_face_index and j < batch_size:\n",
    "    #     frames[j] = frame\n",
    "    #     print(f\"frame: {i}\")\n",
    "    #     j += 1\n",
    "    # elif i in face_index_list and k < batch_size:\n",
    "    #     face_frames[k] = frame\n",
    "    #     print(f\"face_frame: {i}\")\n",
    "    #     k += 1\n",
    "\n",
    "    # if j == batch_size and k == batch_size:\n",
    "    #     break\n",
    "\n",
    "    i += 1\n",
    "    if i == batch_size:\n",
    "        break\n",
    "\n",
    "cap.release()\n",
    "np.save(r\"E:\\Lip_Wise_GFPGAN\\_testData\\Outputs\\frames.npy\", frames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "imgs = np.load(r\"E:\\Lip_Wise_GFPGAN\\_testData\\Outputs\\frames.npy\")\n",
    "plt.imshow(imgs[400][:, :, ::-1])\n",
    "frames_batch = np.array_split(imgs, len(imgs)//4)\n",
    "\n",
    "for i, batch in enumerate(frames_batch):\n",
    "    print(f\"Batch {i}: {batch.shape}\")\n",
    "    plt.figure(figsize=(10, 10))\n",
    "    for j, frame in enumerate(batch):\n",
    "        plt.subplot(2, 2, j+1)\n",
    "        plt.imshow(frame[:, :, ::-1])\n",
    "        plt.axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IMPORTANT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From e:\\Lip_Wise_GFPGAN\\gan\\lib\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "e:\\Lip_Wise_GFPGAN\\gan\\lib\\site-packages\\torchvision\\transforms\\functional_tensor.py:5: UserWarning: The torchvision.transforms.functional_tensor module is deprecated in 0.15 and will be **removed in 0.17**. Please don't rely on it. You probably just need to use APIs in torchvision.transforms.functional or in torchvision.transforms.v2.functional.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "frames_batch_shape: (17, 720, 1280, 3)\n"
     ]
    }
   ],
   "source": [
    "import batch_processors\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "\n",
    "# Creating Boolean mask\n",
    "total_frames = np.arange(0, 500) #500 is frame count\n",
    "no_face_index = np.load(r\"E:\\Lip_Wise_GFPGAN\\temp\\npy_files\\no_face_index.npy\")\n",
    "\n",
    "mask = np.isin(total_frames, no_face_index, invert=True).astype(bool)\n",
    "mask_batch = np.array_split(mask, len(mask)//16, axis=0)\n",
    "\n",
    "# Load the npy file\n",
    "frames = np.load(r\"E:\\Lip_Wise_GFPGAN\\_testData\\Outputs\\frames.npy\")\n",
    "frames_batch = np.array_split(frames, len(frames)//16, axis=0)\n",
    "\n",
    "print(f\"frames_batch_shape: {frames_batch[0].shape}\")\n",
    "\n",
    "frame_numbers = np.arange(0, len(frames))\n",
    "frame_nos_batch = np.array_split(frame_numbers, len(frames)//16, axis=0)\n",
    "\n",
    "bp = batch_processors.BatchProcessors(frames, 0, 16, 16)\n",
    "\n",
    "writer = cv2.VideoWriter(r\"E:\\Lip_Wise_GFPGAN\\_testData\\Outputs\\output.mp4\", cv2.VideoWriter_fourcc(*'mp4v'), 25, (frames.shape[2], frames.shape[1]))\n",
    "\n",
    "for i in range(len(frames_batch)):\n",
    "    frame_nos_to_input = frame_nos_batch[i][mask_batch[i]]\n",
    "    frames_to_input = frames_batch[i][mask_batch[i]]\n",
    "    if len(frames_to_input) == 0:\n",
    "        continue\n",
    "    extracted_faces, original_masks = bp.extract_face_batch(frames_to_input, frame_nos_to_input)\n",
    "    frames_batch[i][mask_batch[i]] = extracted_faces\n",
    "\n",
    "for i in range(len(frames_batch)):\n",
    "    for j in range(len(frames_batch[i])):\n",
    "        writer.write(frames_batch[i][j])\n",
    "\n",
    "writer.release()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color=red>IMPORTANT (Code For Processing batch)</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### A boolean mask can be created using no face index and it can be applied into np array to select specific elements of the array, in this case frames with face"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import batch_processors\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "\n",
    "cap = cv2.VideoCapture(r\"E:\\Lip_Wise_GFPGAN\\_testData\\Inputs\\small_test.mp4\")\n",
    "\n",
    "# Get video properties\n",
    "fps = cap.get(cv2.CAP_PROP_FPS)\n",
    "frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n",
    "width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n",
    "height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n",
    "\n",
    "# Creating Boolean mask\n",
    "total_frames = np.arange(0, frame_count) #500 is frame count\n",
    "no_face_index = np.load(r\"E:\\Lip_Wise_GFPGAN\\temp\\npy_files\\no_face_index.npy\")\n",
    "\n",
    "mask = np.isin(total_frames, no_face_index, invert=True).astype(bool)\n",
    "mask_batch = np.array_split(mask, len(mask)//16, axis=0)\n",
    "\n",
    "writer = cv2.VideoWriter(r\"E:\\Lip_Wise_GFPGAN\\_testData\\Outputs\\output_grand.mp4\", cv2.VideoWriter_fourcc(*'mp4v'), fps, (width, height))\n",
    "\n",
    "# Create an array of frame numbers\n",
    "frame_numbers = np.arange(0, frame_count)\n",
    "frame_nos_batch = np.array_split(frame_numbers, frame_count//16, axis=0)\n",
    "\n",
    "bp = batch_processors.BatchProcessors()\n",
    "\n",
    "images = []\n",
    "batch_no = 0\n",
    "while True:\n",
    "    ret, frame = cap.read()\n",
    "\n",
    "    if not ret:\n",
    "        break\n",
    "\n",
    "    frame_no = int(cap.get(cv2.CAP_PROP_POS_FRAMES))\n",
    "    print(f\"Appending Frame no: {frame_no}\")\n",
    "    images.append(frame)\n",
    "\n",
    "    print(f\"len of images: {len(images)}, len of mask_batch: {len(mask_batch[batch_no])}\")\n",
    "\n",
    "    if len(images) == len(mask_batch[batch_no]):\n",
    "        \n",
    "        frames = np.array(images)\n",
    "        frame_nos_to_input = frame_nos_batch[batch_no][mask_batch[batch_no]]\n",
    "        frames_to_input = frames[mask_batch[batch_no]]\n",
    "        if len(frames_to_input) != 0:\n",
    "            extracted_faces, original_masks = bp.extract_face_batch(frames_to_input, frame_nos_to_input)\n",
    "            frames[mask_batch[batch_no]] = extracted_faces\n",
    "        print(f\"Writing batch no: {batch_no}\")\n",
    "        for frame in frames:\n",
    "            writer.write(frame)\n",
    "        batch_no += 1\n",
    "\n",
    "        images = []\n",
    "\n",
    "cap.release()\n",
    "writer.release()\n",
    "\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1  4  6  4  5 12 14 16  9 10]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n",
    "mask = np.array([0, 1, 1, 0, 0, 1, 1, 1, 0, 0]).astype(bool)\n",
    "\n",
    "# Multiply elements based on the mask and insert them back into the original array\n",
    "arr[mask] = arr[mask]*2\n",
    "\n",
    "print(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10  2 20  4 30 40 50  8  9 10]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "arr1 = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n",
    "arr2 = np.array([10, 20, 30, 40, 50])\n",
    "mask = np.array([True, False, True, False, True, True, True, False, False, False])\n",
    "\n",
    "# Create an iterator for arr2 to cycle through elements\n",
    "# arr2_iter = iter(arr2)\n",
    "\n",
    "# Insert elements from arr2 into arr1 where mask is True\n",
    "# ooo = [next(arr2_iter) for _ in range(mask.sum())]\n",
    "arr1[mask] = arr2\n",
    "\n",
    "print(arr1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True False  True False  True False  True False  True  True]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "arr1 = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n",
    "arr2 = np.array([2, 4, 6, 8])\n",
    "\n",
    "# Create a boolean mask where common elements are False, others are True\n",
    "mask = ~np.isin(arr1, arr2)\n",
    "\n",
    "print(mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import batch_processors\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the npy file\n",
    "frames = np.load(r\"E:\\Lip_Wise_GFPGAN\\_testData\\Outputs\\frames.npy\")\n",
    "\n",
    "bp = batch_processors.BatchProcessors(frames, 0, 16, 16)\n",
    "\n",
    "frame_nos = np.arange(30, 46)\n",
    "# Get the first 16 frames_extraction\n",
    "extracted_faces = bp.extract_face_batch(frames[30:46], frame_nos)\n",
    "\n",
    "# Check if there are at least 16 frames\n",
    "if frames.shape[0] < 16:\n",
    "    raise ValueError('The npy file must contain at least 16 frames.')\n",
    "\n",
    "# Create a 4x4 grid of subplots\n",
    "fig, axs = plt.subplots(4, 4)\n",
    "\n",
    "# Iterate over the first 16 frames\n",
    "for i in range(4):\n",
    "    for j in range(4):\n",
    "        # Plot the frame in the subplot\n",
    "        axs[i, j].imshow(frames[i*4 + j][:, :, ::-1])\n",
    "        axs[i, j].axis('off')  # Hide axes\n",
    "\n",
    "face, face_axs = plt.subplots(4, 4)\n",
    "for i in range(4):\n",
    "    for j in range(4):\n",
    "        # Plot the frame in the subplot\n",
    "        face_axs[i, j].imshow(extracted_faces[i*4 + j][0][:, :, ::-1])\n",
    "        face_axs[i, j].axis('off')  # Hide axes\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  }
 ],
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